发表机构
University of Kassel; Interdisciplinary Research Center for Information Systems Design (ITeG)（卡塞尔大学; 信息系统设计跨学科研究中心（ITeG））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出兼容scikit-learn的Python库ConceptFlow，将其应用于1975-2025年ESC获奖者，通过嵌套线图探究投票模式与音乐特征的关联，揭示两者间的依赖关系。
AI 中文摘要
我们提出ConceptFlow，这是一个兼容scikit-learn的Python库，用于形式概念分析，可从多值形式背景构建并渲染嵌套线图。给定一个多值背景及其属性划分为概念尺度的划分，ConceptFlow会执行概念尺度变换、计算因子格、识别相应子直积的填充节点，并生成交互式可视化。我们将ConceptFlow应用于1975年至2025年的欧洲歌唱大赛（ESC）获奖者，探究投票模式与音乐特征之间的关系。投票支持度由涵盖区域、文化、历史和政治维度的外部尺度捕获，而内部尺度则通过速度和调式捕获音乐特征。生成的嵌套线图揭示了两个尺度之间的关联，展现了获奖作品的投票方式与其共享音乐属性之间的依赖关系。
英文摘要
We present ConceptFlow, a scikit-learn-compatible Python library for Formal Concept Analysis that constructs and renders nested line diagrams from many-valued formal contexts. Given a many-valued context and a partition of its attributes into conceptual scales, ConceptFlow performs conceptual scaling, computes the factor lattices, identifies filled nodes of the corresponding subdirect product, and produces an interactive visualization. We apply ConceptFlow to the winners of the Eurovision Song Contest from 1975 to 2025, exploring relationships between voting patterns and musical characteristics. Voting support is captured by an outer scale spanning regional, cultural, historical, and political dimensions, while an inner scale captures musical characteristics via tempo and key. The resulting nested line diagram reveals implications across both scales, exposing dependencies between how winning entries were voted for and the musical properties they share.
CommentsAccepted for CONCEPTS 2026 Data Analysis Showcase (CDAS)
